• Steel and Composite Structures
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• v.44 no.3
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• pp.407-421
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• 2022

Prefabricated hybrid wind turbine towers (WTTs) are promising due to height increase. This study proposes the use of ultra-high performance concrete (UHPC) to develop a new type of WTT without the need to use reinforcement. It is demonstrated that the UHPC WTT structure without reinforcing bars could achieve performance similar to that of reinforced concrete WTTs. To simplify the design of WTT, a design approach for the calculation of stresses at the horizontal joints of a WTT is proposed. The stress distribution near the region of the horizontal joint of the WTT structure under normal operating conditions and different load actions is studied using the proposed approach, which is validated by the finite element method. A further parametric study shows that the degree of prestressing and the bending moment both significantly affect the principal stress. The shear-to-torsion ratio also shows a significant influence on the principal tensile stress.

• Smart Structures and Systems
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• v.30 no.4
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• pp.385-395
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• 2022

Detecting cracks on a concrete structure is crucial for structural maintenance, a crack being an indicator of possible damage. Conventional crack detection methods which include visual inspection and non-destructive equipment, are typically limited to a small region and require time-consuming processes. Recently, to reduce the human intervention in the inspections, various researchers have sought computer vision-based crack analyses: One class is filter-based methods, which effectively transforms the image to detect crack edges. The other class is using deep-learning algorithms. For example, convolutional neural networks have shown high precision in identifying cracks in an image. However, when the objective is to classify not only the existence of crack but also the types of cracks, only a few studies have been reported, limiting their practical use. Thus, the presented study develops an image processing procedure that detects cracks and classifies crack types; whether the image contains a crazing-type, single crack, or multiple cracks. The properties and steps in the algorithm have been developed using field-obtained images. Subsequently, the algorithm is validated from additional 227 images obtained from an open database. For test datasets, the proposed algorithm showed accuracy of 92.8% in average. In summary, the developed algorithm can precisely classify crazing-type images, while some single crack images may misclassify into multiple cracks, yielding conservative results. As a result, the successful results of the presented study show potentials of using vision-based technologies for providing crack information with reduced human intervention.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.112-117
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• 2023

This study aims to find out what tourists' interests and perceptions are like through online big data. Big data for a total of five years from 2018 to 2022 were collected using the Textom program. Sentiment analysis was performed with the collected data. Sentiment analysis expresses the necessity and emotions of city tours in online reviews written by tourists using city tours. The purpose of this study is to extract and analyze keywords representing satisfaction. The sentiment analysis program provided by the big data analysis platform "TEXTOM" was used to study positives and negatives based on sentiment analysis of tourists' online reviews. Sentiment analysis was conducted by collecting reviews related to the city tour. The degree of positive and negative emotions for the city tour was investigated and what emotional words were analyzed for each item. As a result of big data sentiment analysis to examine the emotions and sentiments of tourists about the city tour, 93.8% positive and 6.2% negative, indicating that more than half of the tourists are positively aware. This paper collects tourists' opinions based on the analyzed sentiment analysis, understands the quality characteristics of city tours based on the analysis using the collected data, and sentiment analysis provides important information to the city tour platform for each region.

• International journal of advanced smart convergence
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• v.11 no.1
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• pp.101-110
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• 2022

The purpose of this study is to evaluate the efficiency of financial support for rural industry revitalization in eastern China. Comparative analysis of the efficiency of provincial financial support for rural industrial revitalization in eastern China can provide reference for various provinces to formulate financial policies for rural revitalization. In the research process, 5 evaluation indicators were selected using the panel data of the 2016-2021 "China Financial Statistical Yearbook" and "China Statistical Yearbook", and the DEA and Malmquist index methods were used for calculation. The results show that the average efficiency of financial support for rural revitalization in the 10 eastern provinces from 2015 to 2020 was generally higher, with the efficiency values all higher than 0.8, and reached 0.908 in 2017. The comprehensive efficiency of financial support for rural industry revitalization in Tianjin, Shanghai and Hainan has reached the best. From 2015 to 2020, the total factor productivity of financial support for rural industry revitalization in the eastern region has a "V"-shaped fluctuation. Total factor productivity has the fastest growth. The provinces are Beijing, Hebei and Shandong showing negative growth. It is recommended that relevant provinces improve their strategies for financial support for the revitalization of rural industries. The scope of future research should be expanded to most areas of China and the evaluation indicators should be optimized.

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.120-128
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• 2022

While online social interactions have been more prevalent with the increased popularity of Metaverse platforms, little has been studied the effects of facial expressions in virtual reality (VR), which is known to play a key role in social contexts. To understand the importance of presenting facial expressions of a virtual avatar under different contexts, we conducted a user study with 24 participants where they were asked to have a conversation and play a charades game with an avatar with and without facial expressions. The results show that participants tend to gaze at the face region for the majority of the time when having a conversation or trying to guess emotion-related keywords when playing charades regardless of the presence of facial expressions. Yet, we confirmed that participants prefer to see facial expressions in virtual reality as well as in real-world scenarios as it helps them to better understand the contexts and to have more immersive and focused experiences.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.912-919
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• 2022

The study is an image-based assessment that uses image processing techniques to determine the condition of concrete with surface cracks. The preparations of the dataset include resizing and image filtering to ensure statistical homogeneity and noise reduction. The image dataset is then segmented, making it more suited for extracting important features and easier to evaluate. The image is transformed into grayscale which removes the hue and saturation but retains the luminance. To create a clean edge map, the edge detection process is utilized to extract the major edge features of the image. The Otsu method is used to minimize intraclass variation between black and white pixels. Additionally, the median filter was employed to reduce noise while keeping the borders of the image. Image processing techniques are used to enhance the significant features of the concrete image, especially the defects. In this study, the tonal zones of the histogram and its properties are used to analyze the condition of the concrete. By examining the histogram, the viewer will be able to determine the information on the image through the number of pixels associated and each tonal characteristic on a graph. The features of the five tonal zones of the histogram which implies the qualities of the concrete image may be evaluated based on the quality of the contrast, brightness, highlights, shadow spikes, or the condition of the shadow region that corresponds to the foreground.

• Smart Structures and Systems
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• v.30 no.5
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• pp.501-511
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• 2022

Three-dimensional (3D) models have become crucial for improving civil infrastructure analysis, and they can be used for various purposes such as damage detection, risk estimation, resolving potential safety issues, alarm detection, and structural health monitoring. 3D point cloud data is used not only to make visual models but also to analyze the states of structures and to monitor them using semantic data. This study proposes automating the generation of high-quality 3D point cloud data and removing noise using deep learning algorithms. In this study, large-format aerial images of civilian infrastructure, such as cut slopes and dams, which were captured by drones, were used to develop a workflow for automatically generating a 3D point cloud model. Through image cropping, downscaling/upscaling, semantic segmentation, generation of segmentation masks, and implementation of region extraction algorithms, the generation of the point cloud was automated. Compared with the method wherein the point cloud model is generated from raw images, our method could effectively improve the quality of the model, remove noise, and reduce the processing time. The results showed that the size of the 3D point cloud model created using the proposed method was significantly reduced; the number of points was reduced by 20-50%, and distant points were recognized as noise. This method can be applied to the automatic generation of high-quality 3D point cloud models of civil infrastructures using aerial imagery.

• Smart Structures and Systems
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• v.33 no.2
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• pp.105-118
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• 2024

In order to realize tiny bridge crack discovery by UAV-based machine vision, a novel method combining deep learning and tensor voting is proposed. Firstly, the grid images of crack are detected and descripted based on SE-ResNet50 to generate feature points. Then, the probability significance map of crack image is calculated by tensor voting with feature points, which can define the direction and region of crack. Further, the crack detection anchor box is formed by non-maximum suppression from the probability significance map, which can improve the robustness of tiny crack detection. Finally, a case study is carried out to demonstrate the effectiveness of the proposed method in the Xiangjiang-River bridge inspection. Compared with the original tensor voting algorithm, the proposed method has higher accuracy in the situation of only 1-2 pixels width crack and the existence of edge blur, crack discontinuity, which is suitable for UAV-based bridge crack discovery.

• Journal of Korea Multimedia Society
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• v.16 no.8
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• pp.982-993
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• 2013

Currency crisis, in order to solve the problem of the polarization of economic and serious youth unemployment rate, to prepare for the smart new economic era, and like to support and nurture the one-persion creating company of mobile app industry of central and local government people policies are underway. To be able to contribute to the establishment of the success of mobile app One-person creating company, to target the mobile app One-person creating company int the Busan region, we conducted an empirical study of success factors, and thus, support these solutions, more effective and efficient in an attempt to try to seek the support measures, was carried out this research. In this study, results derive a research three hypotheses about the success factors of One-person creating company through literature discussion, were investigated on the basis of empirical data of the mobile app one-persion creative company of Busan region of 51 individual, establishment of company period is longer, the number of organizations employee is large and attract a lot of external funds, and was able to find the tendency of more entrepreneurs receives an education, may increase an outcome of business. In this study, it is possible to obtain the policy implications as follows. First, to attract investment funds and attract government funding to support the funding of One-person creating company, it is necessary to pay attention more. Second, policy founder to an education about the management on an ongoing basis is needed than the education of marketing and technology for human resources development. Future, you will be able to expand across the country study company, we have established a policy of mobile app industry of national dimension, in this foundation, will be expanded to all industries.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.3
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• pp.95-105
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• 2019

In recent years, image processing techniques for detecting road surface damaged spot have been actively researched. Especially, it is mainly used to acquire images through a smart phone or a black box that can be mounted in a vehicle and recognize the road surface damaged region in the image using several algorithms. In addition, in conjunction with the GPS module, the exact damaged location can be obtained. The most important technology is image processing algorithm. Recently, algorithms based on artificial intelligence have been attracting attention as research topics. In this paper, we will also discuss artificial intelligence image processing algorithms. Among them, an object detection method based on an region-based convolution neural networks method is used. To improve the recognition performance of road surface damage objects, 600 road surface damaged images and 1500 general road driving images are added to the learning database. Also, supervised learning using background object recognition method is performed to reduce false alarm and missing rate in road surface damage detection. As a result, we introduce a new method that improves the recognition performance of the algorithm to 8.66% based on average value of mAP through the same test database.